{"id":"W1966836840","doi":"10.1109/icdm.2013.132","title":"Explaining Outliers by Subspace Separability","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Anomaly detection; Computer science; Linear subspace; Subspace topology; Focus (optics); Data mining; Heuristic; Domain (mathematical analysis); Artificial intelligence; Pattern recognition (psychology); Ranking (information retrieval); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002114088,0.001576435,0.001399746,0.004746236,0.001012206,0.002530348,0.001812844,0.001766076,0.001932476],"category_scores_gemma":[0.01287152,0.0006004442,0.001792865,0.004334822,0.001784596,0.003541418,0.003155752,0.002362794,0.0009669127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008148662,"about_ca_system_score_gemma":0.001457917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003643189,"about_ca_topic_score_gemma":0.003289657,"domain_scores_codex":[0.9973506,0.0006847387,0.0001883382,0.0005879933,0.0009377786,0.0002505455],"domain_scores_gemma":[0.9914265,0.004226948,0.001412803,0.00135135,0.001326342,0.0002561326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005003942,0.0002241116,0.01819447,0.0004459172,0.0002763242,0.0007327285,0.001025351,0.3168247,0.01740501,0.07661588,0.009059815,0.5586954],"study_design_scores_gemma":[0.00002933382,0.00006625292,0.001961568,0.00003308609,0.00003947474,0.0003139067,0.0002561629,0.8983876,0.00628761,0.0879356,0.00462668,0.00006279619],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01039255,0.0001748148,0.9878801,0.0001750021,0.00002125059,0.00004363636,0.0001223216,0.0007623173,0.0004280197],"genre_scores_gemma":[0.2937771,0.0005901831,0.7021039,0.0001426552,0.0001670969,0.0001543675,0.001259455,0.0003007888,0.00150446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004746236,"threshold_uncertainty_score":0.01118052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006427386465298589,"score_gpt":0.2247912570320667,"score_spread":0.2183638705667681,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}